Wireless communications systems are becoming an increasingly integral, aspect of modern communications. In order to ensure quality of service and end-user satisfaction, efficient resource allocation and management strategies are required. In multi-user wireless data systems, scheduling algorithms are used to give priority to users and to schedule transmissions to and from those users. For such systems where user channel conditions change over time, such scheduling algorithms take advantage of channel variations by giving priority to the users with better channel conditions over a given time frame, while at the same time ensuring fairness among the user population.
While traditional wireless networks have primarily carried voice traffic, current and next-generation wireless networks are becoming increasingly data-centric due to the increased popularity of data applications using protocols such as the Transmission Control Protocol (TCP). As such, future wireless networks must increasingly be able to efficiently allocate resources between both voice and data traffic. However, such efficiency can be difficult to achieve because data applications are fundamentally different from traditional voice applications, both in terms of the traffic characteristics and the quality of service requirements. This difference stems from the fact that, in general, voice applications typically require a constant transmission rate, independent of the network loading and the wireless channel quality. Reliable communication in such voice applications is generally achieved through power control to alleviate adverse channel conditions. On the other hand, in data applications, performance as perceived by the end-user is closely related to the network-layer throughput, the transaction time for initiating a connection and the transaction time for transmitting the data. Both the throughput and transaction time for data transmissions are dependent upon the channel quality, the network load and the resource allocation (scheduling) strategy.
Data applications are typically more delay-tolerant than voice applications and are able to accept a marginal increase in delay to achieve improved long-term throughput and greater energy efficiency. For example, email communications are much less sensitive to delays and interruptions in transmission than are voice communications. Internet access and file transfers, likewise, can tolerate a bursty communications channel, as long as reasonable response times and, reasonable average throughputs are maintained. Further, due to increased buffering typically available on data devices relative to voice devices, and due to the substantially unidirectional nature of the communications, even streaming data applications exhibit a greater robustness to data interruptions than do voice communications. This relative delay tolerance of data traffic, in addition to the bursty nature of data traffic (i.e., packets of data in a transmission tend to be transmitted in bursts), allows for flexible transmission scheduling strategies in order to achieve greater efficiency of the limited network resources.
To meet the specific demands of wireless data systems, several well-known scheduling strategies have been used to maximize the packet data throughput subject to various conditions. One such strategy is to schedule data transmissions based on the relative channel quality of the different users. At its simplest, such a strategy would schedule the user with the best instantaneous channel quality, thereby maximizing overall system throughput. However, such a strategy would have the effect of preventing weaker users from being scheduled for transmission and would, therefore, lead to potential unfairness across the user population.
It has been widely recognized that it is important to allocate resources to achieve an acceptable balance between system performance and fairness among users. A well-known algorithm that achieves this objective is the Proportional Fair algorithm that is, for example, used in CDMA 1×EV-DO systems. Instead of simply scheduling the user with the best instantaneous channel quality, this algorithm schedules users in a fair manner to ensure that even users with relatively weak channel conditions receive adequate data throughputs, proportionally to the relative strengths of their channel quality. The Proportional Fair algorithm achieves fairness, in general, by scheduling users for transmission close to the relative peaks of their channel quality. As a result, system throughput is increased because of the fact that, the more users there are in the system, the more probable it is that at least one of the users' channel condition is close to its peak value.